Overview

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Responsibilities

• Design, develop, and deploy machine learning models and AI-driven systems into production environments.
• Build and maintain scalable data pipelines for model training, evaluation, and inference.
• Fine-tune and optimize large language models (LLMs) and other deep learning architectures for performance, latency, and cost.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embedding generation, retrieval strategies (dense, sparse, hybrid), and re-ranking.
• Build and manage vector databases (e.g., Pinecone, Weaviate, Milvus, Qdrant, pgvector, FAISS) for efficient similarity search and embeddings storage at scale.
• Design and build agentic AI systems — multi-step, tool-calling, and multi-agent workflows using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
• Implement durable/long-running agentic workflows using orchestration engines such as Temporal (or similar, e.g., Airflow, Step Functions) to manage state, retries, and reliability in multi-step AI processes.
• Integrate AI/ML and generative AI models into applications via APIs, microservices, or embedded systems.
• Implement MLOps/LLMOps practices: CI/CD for ML, prompt/version management, evaluation harnesses, model monitoring, and automated retraining.
• Design effective prompt engineering strategies, few-shot examples, and structured output/function-calling schemas for LLM applications.
• Collaborate with cross-functional teams (product, engineering, data science) to translate business requirements into technical solutions.
• Conduct experiments, A/B tests, and performance/quality evaluations (hallucination rate, groundedness, relevance) to validate model and RAG pipeline effectiveness.
• Ensure responsible AI practices, including bias mitigation, explainability, guardrails, and compliance with data privacy/security standards.
• Stay current with emerging AI/ML/GenAI research, tools, and frameworks, and evaluate their applicability.
• Document architectures, workflows, and technical decisions for maintainability and knowledge sharing.
• As a member of AI team, the candidate is expected to participate and drive deliverables associated with Business Use cases.
• Understand the Business requirement and execute the solutioning and ensue the delivery commitments are delivered on time and schedule.
• AI Delivery Planning & Execution
• DevSecOps & Release Management
• Risk, Compliance & Governance
• Cross-functional Team Leadership
• Stakeholder & Vendor Collaboration
• Talent Development & Coaching
• Ownership of the delivery, highlighting various risks on a timely manner to the stakeholders.
• Identifying proper remediation plan for the risks with proper risk roadmap.
• Awareness and understanding of the regulatory framework, in which the Group operates, and the regulatory requirements and expectations relevant to the role.
• Display exemplary conduct and live by the Group’s Values and Code of Conduct.
• Take personal responsibility for embedding the highest standards of ethics, including regulatory and business conduct, across Standard Chartered Bank. This includes understanding and ensuring compliance with, in letter and spirit, all applicable laws, regulations, guidelines and the Group Code of Conduct.
• Effectively and collaboratively identify, escalate, mitigate and resolve risk, conduct and compliance matters.
• Business Stakeholders

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related discipline; specialization in AI/ML preferred
• 5+ years’ experience in software delivery, with proven track record in delivering complex AI/ML or data-driven solutions
• Strong proficiency in Python and AI/ML frameworks.
• Hands-on experience with LLMs, RAG architectures, and embeddings/vector search.
• Practical experience building agentic workflows (tool use, planning/reasoning loops, multi-agent orchestration).
• Familiarity with vector databases and semantic search infrastructure.
• Solid understanding of data structures, algorithms, and software engineering best practices.
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
• Strong experience in managing end-to-end SDLC, delivery governance, and cross-functional engineering teams
• Exposure to AI/ML ecosystems, MLOps, cloud platforms (Azure/AWS), and modern engineering practices
• Demonstrated leadership capability, stakeholder management, and ability to operate in regulated enterprise environments (risk, audit, governance)
• Strong communication skills to explain technical concepts to non-technical stakeholders.
• Ability to work independently and as part of a cross-functional team. Curiosity and a continuous learning mindset given the fast-evolving AI/GenAI landscape.

Benefits

• Core bank funding for retirement savings, medical and life insurance, with flexible and voluntary benefits available in some locations.
• Time-off including annual leave, parental/maternity (20 weeks), sabbatical (12 months maximum) and volunteering leave (3 days), along with minimum global standards for annual and public holiday, which is combined to 30 days minimum.
• Flexible working options based around home and office locations, with flexible working patterns.
• Proactive wellbeing support through Unmind, a market-leading digital wellbeing platform, development courses for resilience and other human skills, global Employee Assistance Programme, sick leave, mental health first-aiders and all sorts of self-help toolkits A continuous learning culture to support your growth, with opportunities to reskill and upskill and access to physical, virtual and digital learning.
• Being part of an inclusive and values driven organisation, one that embraces and celebrates our unique diversity, across our teams, business functions and geographies - everyone feels respected and can realise their full potential.
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